watershed_process.py
2.05 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
import numpy as np
from scipy import ndimage
from scipy.ndimage import watershed_ift, generate_binary_structure
from skimage.morphology import watershed
def get_LUT_value(data, window, level):
shape = data.shape
data_ = data.ravel()
data = np.piecewise(data_,
[data_ <= (level - 0.5 - (window-1)/2),
data_ > (level - 0.5 + (window-1)/2)],
[0, window, lambda data_: ((data_ - (level - 0.5))/(window-1) + 0.5)*(window)])
data.shape = shape
return data
def do_watershed(image, markers, tfile, shape, bstruct, algorithm, mg_size, use_ww_wl, wl, ww, q):
mask = np.memmap(tfile, shape=shape, dtype='uint8', mode='r+')
if use_ww_wl:
if algorithm == 'Watershed':
tmp_image = ndimage.morphological_gradient(
get_LUT_value(image, ww, wl).astype('uint16'),
mg_size)
tmp_mask = watershed(tmp_image, markers.astype('int16'), bstruct)
else:
tmp_image = get_LUT_value(image, ww, wl).astype('uint16')
#tmp_image = ndimage.gaussian_filter(tmp_image, self.config.mg_size)
#tmp_image = ndimage.morphological_gradient(
#get_LUT_value(image, ww, wl).astype('uint16'),
#self.config.mg_size)
tmp_mask = watershed_ift(tmp_image, markers.astype('int16'), bstruct)
else:
if algorithm == 'Watershed':
tmp_image = ndimage.morphological_gradient((image - image.min()).astype('uint16'), mg_size)
tmp_mask = watershed(tmp_image, markers.astype('int16'), bstruct)
else:
tmp_image = (image - image.min()).astype('uint16')
#tmp_image = ndimage.gaussian_filter(tmp_image, self.config.mg_size)
#tmp_image = ndimage.morphological_gradient((image - image.min()).astype('uint16'), self.config.mg_size)
tmp_mask = watershed_ift(tmp_image, markers.astype('int8'), bstruct)
mask[:] = tmp_mask
mask.flush()
q.put(1)